Hemodynamic performance evaluation of neonatal <scp>ECMO</scp> double lumen cannula using <scp>fluid–structure</scp> interaction
Bibliographic record
Abstract
Extra corporeal membrane oxygenation (ECMO) is an artificial oxygenation facility, employed in situations of cardio-pulmonary failure. Some diseases i.e., acute respiratory distress syndrome, pulmonary hypertension, corona virus disease (COVID-19) etc. affect oxygenation performance of the lungs thus requiring the need of artificial oxygenation. Critical care teams used ECMO technique during the COVID-19 pandemic to support the heart and lungs of COVID-19 patients who had an acute respiratory or cardiac failure. Double Lumen Cannula (DLC) is one of the most critical components of ECMO as it resides inside the patient and, connects patient with external oxygenation circuit. DLC facilitates delivery and drainage of blood from the patient's body. DLC is characterized by delicate balance of internal and external flows inside a limited space of the right atrium (RA). An optimal performance of the DLC necessitates structural stability under biological and hemodynamic loads, a fact that has been overlooked by previously published studies. In the past, many researchers experimentally and computationally investigated the hemodynamic performance of DLC by employing Eulerian approach, which evaluate instantaneous blood damage without considering blood shear exposure history (qualitative assessment only). The present study is an attempt to address the aforementioned limitations of the previous studies by employing Lagrangian (quantitative assessment) and incorporating the effect of fluid-structure interaction (FSI) to study the hemodynamic performance of neonatal DLC. The study was performed by solving three-dimensional continuity, momentum, and structural mechanics equation(s) by numerical methods for the blood flow through neonatal DLC. A two-way coupled FSI analysis was performed to analyze the effect of DLC structural deformation on its hemodynamic performance. Results show that the return lumen was the most critical section with maximum pressure drop, velocity, shear stresses, and blood damage. Recirculation and residence time of blood in the right atrium (RA) increases with increasing blood flow rates. Considering the structural deformation has led to higher blood damage inside the DLC-atrium system. Maximum Von-Mises stress was present on the side edges of the return lumen that showed direct proportionality with the blood flow rate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".